Essentials of Econometrics
5th Edition
ST
UV
SOLUTIONS
IA
MANUAL
_A
PP
Damodar Gujarati
RO
Dawn Porter
VE
Comprehensive Solutions Manual for
D?
Instructors and Students
© Damodar Gujarati & Dawn Porter. All rights reserved. Reproduction or distribution
without permission is prohibited.
©MedConnoisseur
, Solutions Manual for Essentials of Econometrics, 5e Damodar Gujarati,
Dawn Porter (All Chapters)
CHAPTER 1
THE NATURE AND SCOPE OF ECONOMETRICS
QUESTIONS
1.1. (a) Other things remaining the same, the higher the tax rate is, the lower
ST
the price of a house will be.
(b) Assume that the data are cross-sectional, involving several residential
communities with differing tax rates.
UV
(c) Yi = B1 + B2 X i
where Y = price of the house and X = tax rate
(d) Yi = B1 + B2 X i + ui
(e) Given the sample, one can use OLS to estimate the parameters of the
IA
model.
(f) Aside from the tax rate, other factors that affect house prices are
_A
mortgage interest rates, house size, buyers’ family income, the state of the
economy, the local crime rate, etc. Such variables may be included in a more
detailed multiple regression model.
(g) A priori, B2 < 0. Therefore, one can test H0 : B2 ≥ 0 against H1 : B2 < 0.
PP
(h) The estimated regression can be used to predict the average price of a
house in a community, given the tax rate in that community. Of course, it
RO
is assumed that all other factors stay the same.
1.2. Econometricians are now routinely employed in government and business
to estimate and / or forecast (1) price and cost elasticities, (2) production
and cost functions, and (3) demand functions for goods and services, etc.
VE
Econometric forecasting is a growth industry.
1.3. The economy will be bolstered if the increase in the money supply leads to
a reduction in the interest rate which will lead to more investment activity
D?
and, therefore, to more output and more employment. If the increase in the
money supply, however, leads to inflation, the preceding result may
1
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, not occur. The job of the econometrician will be to develop a model to
predict the effect of the increase in the money supply on inflation, interest
rate, employment, etc.
1.4. As a matter of fact, on October 1, 1993 the Federal Government did increase
the gasoline tax by 4 cents. Since gasoline and cars are complementary
products, economic theory suggests that an increase in the price of gasoline
ST
will not only lead to a decline in the demand for gasoline but also in the
demand for cars, ceteris paribus. The Ford Motor Companymay be advised
to produce more fuel-efficient cars to stave off a serious decline in the
UV
demand for its cars. An automobile demand function will provide numerical
estimates of the effect of gasoline tax on the demandfor automobiles.
1.5.
There are many alternative designs possible. However, to keep things
IA
simple, and discuss just a basic idea of the design, we could think of using
an econometric model known as Autoregressive Distributed Lag (ARDL)
of the form:
_A
∆yt = α + β∆yt −1 + θ 0 ∆gt + θ1∆gt −1 + γ 0 ∆τ tp + +γ 1∆τ tp−1 + δ 0 ∆τ tc + δ1∆τ tc−1 + ut
where
Yt − Yt −1
yt = = real GDP growth rate in year t;
PP
Yt −1
Gt − Gt −1
gt = = real government infrastructure investment growth rate in
Gt −1
RO
year t;
τ tp = personal income tax rate in year t;
τ tc = corporate income tax rate in year t;
∆yt = yt − yt −1 = change in real GDP growth rate in year t;
VE
∆g t = g t − g t −1 = change in real government infrastructure investment growth
rate in year t;
∆τ tp = τ tp − τ tp−1 = change in personal income tax rate in year t;
D?
∆τ tc = τ tc − τ tc−1 = change in corporate income tax rate in year t.
Expected signs and magnitudes of the parameters the regression model:
2
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, 0 ≤ β ≤ 1;
θ 0 ,θ1 > 0;
γ 0 , γ 1 , δ 0 , δ1 < 0.
Note that a more realistic model would include a larger number of lags of the
regressors than included in our model, and would also include additional
regressors known as control variables which could be determined based on
economic theory.
ST
Now, based on our specified model above, short run and long run economic
consequence of 1 unit increase in ∆g t , ∆τ tp , and ∆τ tc , respectively, can be
determined by using partial derivatives as follows.
UV
Short run economic consequences of a 1 unit increase in ∆g t are given by:
∂∆yt
= θ0 ;
∂∆gt
∂∆yt +1 ∂ ( β∆yt + θ1∆g t )
= = βθ 0 + θ1;
IA
∂∆g t ∂∆g t
∂∆yt + 2 ∂ ( β∆yt +1 )
= = β ( βθ 0 + θ1 );
∂∆g t ∂∆g t
∂∆yt + 3 ∂ ( β∆yt + 2 ) ∂ (∆yt + 2 )
_A
= =β = β 2 ( βθ 0 + θ1 );
∂∆g t ∂∆g t ∂∆g t
and so on.
Long run consequence is the sum of short run consequences
PP
= θ 0 + ( βθ 0 + θ1 ) + β ( βθ 0 + θ1 ) + β 2 ( βθ 0 + θ1 ) + ...
= θ 0 + βθ 0 + θ1 + β 2θ 0 + βθ1 + β 3θ 0 + β 2θ1 + ...
= θ 0 (1 + β + β 2 + β 3 + ...) + θ1 (1 + β + β 2 + ...)
θ0 θ
RO
= + 1
1− β 1− β
θ +θ
= 0 1.
1− β
Analogously, short run economic consequences of a 1 unit increase in ∆τ tp are
VE
given by:
∂∆yt
= γ 0;
∂∆τ tp
∂∆yt +1 ∂ ( β∆yt + γ 1∆τ tp )
D?
= = βγ 0 + γ 1;
∂∆τ tp ∂∆τ tp
3
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5th Edition
ST
UV
SOLUTIONS
IA
MANUAL
_A
PP
Damodar Gujarati
RO
Dawn Porter
VE
Comprehensive Solutions Manual for
D?
Instructors and Students
© Damodar Gujarati & Dawn Porter. All rights reserved. Reproduction or distribution
without permission is prohibited.
©MedConnoisseur
, Solutions Manual for Essentials of Econometrics, 5e Damodar Gujarati,
Dawn Porter (All Chapters)
CHAPTER 1
THE NATURE AND SCOPE OF ECONOMETRICS
QUESTIONS
1.1. (a) Other things remaining the same, the higher the tax rate is, the lower
ST
the price of a house will be.
(b) Assume that the data are cross-sectional, involving several residential
communities with differing tax rates.
UV
(c) Yi = B1 + B2 X i
where Y = price of the house and X = tax rate
(d) Yi = B1 + B2 X i + ui
(e) Given the sample, one can use OLS to estimate the parameters of the
IA
model.
(f) Aside from the tax rate, other factors that affect house prices are
_A
mortgage interest rates, house size, buyers’ family income, the state of the
economy, the local crime rate, etc. Such variables may be included in a more
detailed multiple regression model.
(g) A priori, B2 < 0. Therefore, one can test H0 : B2 ≥ 0 against H1 : B2 < 0.
PP
(h) The estimated regression can be used to predict the average price of a
house in a community, given the tax rate in that community. Of course, it
RO
is assumed that all other factors stay the same.
1.2. Econometricians are now routinely employed in government and business
to estimate and / or forecast (1) price and cost elasticities, (2) production
and cost functions, and (3) demand functions for goods and services, etc.
VE
Econometric forecasting is a growth industry.
1.3. The economy will be bolstered if the increase in the money supply leads to
a reduction in the interest rate which will lead to more investment activity
D?
and, therefore, to more output and more employment. If the increase in the
money supply, however, leads to inflation, the preceding result may
1
Downloaded by: tutorsection | Want to earn $1.236
Distribution of this document is illegal extra per year?
, not occur. The job of the econometrician will be to develop a model to
predict the effect of the increase in the money supply on inflation, interest
rate, employment, etc.
1.4. As a matter of fact, on October 1, 1993 the Federal Government did increase
the gasoline tax by 4 cents. Since gasoline and cars are complementary
products, economic theory suggests that an increase in the price of gasoline
ST
will not only lead to a decline in the demand for gasoline but also in the
demand for cars, ceteris paribus. The Ford Motor Companymay be advised
to produce more fuel-efficient cars to stave off a serious decline in the
UV
demand for its cars. An automobile demand function will provide numerical
estimates of the effect of gasoline tax on the demandfor automobiles.
1.5.
There are many alternative designs possible. However, to keep things
IA
simple, and discuss just a basic idea of the design, we could think of using
an econometric model known as Autoregressive Distributed Lag (ARDL)
of the form:
_A
∆yt = α + β∆yt −1 + θ 0 ∆gt + θ1∆gt −1 + γ 0 ∆τ tp + +γ 1∆τ tp−1 + δ 0 ∆τ tc + δ1∆τ tc−1 + ut
where
Yt − Yt −1
yt = = real GDP growth rate in year t;
PP
Yt −1
Gt − Gt −1
gt = = real government infrastructure investment growth rate in
Gt −1
RO
year t;
τ tp = personal income tax rate in year t;
τ tc = corporate income tax rate in year t;
∆yt = yt − yt −1 = change in real GDP growth rate in year t;
VE
∆g t = g t − g t −1 = change in real government infrastructure investment growth
rate in year t;
∆τ tp = τ tp − τ tp−1 = change in personal income tax rate in year t;
D?
∆τ tc = τ tc − τ tc−1 = change in corporate income tax rate in year t.
Expected signs and magnitudes of the parameters the regression model:
2
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Distribution of this document is illegal extra per year?
, 0 ≤ β ≤ 1;
θ 0 ,θ1 > 0;
γ 0 , γ 1 , δ 0 , δ1 < 0.
Note that a more realistic model would include a larger number of lags of the
regressors than included in our model, and would also include additional
regressors known as control variables which could be determined based on
economic theory.
ST
Now, based on our specified model above, short run and long run economic
consequence of 1 unit increase in ∆g t , ∆τ tp , and ∆τ tc , respectively, can be
determined by using partial derivatives as follows.
UV
Short run economic consequences of a 1 unit increase in ∆g t are given by:
∂∆yt
= θ0 ;
∂∆gt
∂∆yt +1 ∂ ( β∆yt + θ1∆g t )
= = βθ 0 + θ1;
IA
∂∆g t ∂∆g t
∂∆yt + 2 ∂ ( β∆yt +1 )
= = β ( βθ 0 + θ1 );
∂∆g t ∂∆g t
∂∆yt + 3 ∂ ( β∆yt + 2 ) ∂ (∆yt + 2 )
_A
= =β = β 2 ( βθ 0 + θ1 );
∂∆g t ∂∆g t ∂∆g t
and so on.
Long run consequence is the sum of short run consequences
PP
= θ 0 + ( βθ 0 + θ1 ) + β ( βθ 0 + θ1 ) + β 2 ( βθ 0 + θ1 ) + ...
= θ 0 + βθ 0 + θ1 + β 2θ 0 + βθ1 + β 3θ 0 + β 2θ1 + ...
= θ 0 (1 + β + β 2 + β 3 + ...) + θ1 (1 + β + β 2 + ...)
θ0 θ
RO
= + 1
1− β 1− β
θ +θ
= 0 1.
1− β
Analogously, short run economic consequences of a 1 unit increase in ∆τ tp are
VE
given by:
∂∆yt
= γ 0;
∂∆τ tp
∂∆yt +1 ∂ ( β∆yt + γ 1∆τ tp )
D?
= = βγ 0 + γ 1;
∂∆τ tp ∂∆τ tp
3
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